arXiv subjects

David Mell

Publications and source records attributed to David Mell.

2 recordsLinked to original sources

arXiv2026

PopPy: Opportunistically Exploiting Parallelism in Python Compound AI Applications

Compound AI applications, which compose calls to ML models using a general-purpose programming language like Python, are widely used for a variety of user-facing tasks, from software engineering to enterprise automation, making their end-to-end latency a critical bottleneck. In contrast to traditional applications, execution time is dominated by the external components, which cannot be handled by traditional language optimization systems, like optimizing compilers. To address this problem, we develop PopPy, a system that can uncover parallelization opportunities in Python applications that invoke these heavy external components, including those used in compound AI applications. PopPy supports a very expressive fragment of Python and requires minimal developer input to uncover parallelism. It combines an ahead-of-time compiler with a runtime, addressing three key challenges in extracting parallelism from Python applications: language complexity, dynamic dispatch, and variable mutation. On a set of real-world compound AI applications, PopPy achieves up to $6.4\times$ speedups in end-to-end execution time compared to standard Python execution while preserving the sequential program semantics.

cs.DC↗

arXiv2025

Quasar: A Programming Language Specialized for LLM Code Actions

Large language models (LLMs) often call external tools to solve tasks. One effective strategy is for LLMs to write code, enabling them to use complex control flow such as conditionals and loops. Such code actions are typically represented as Python code, since LLMs are proficient at writing it. However, many programming language features that would support more effective code actions are difficult to implement for Python. We propose separating internal code that captures program logic from external calls to tools that interact with the world. New features can then easily be implemented by (1) annotating external calls with the effects relevant to that feature, and (2) modifying the execution of the internal code to track this information. We develop a novel programming language, Quasar, that implements this idea. To illustrate its utility, we implement several useful features on top of Quasar to enhance code actions: access control with batched user queries to improve security, autoparallelization of external calls to reduce latency, and conformal prediction for uncertainty quantification to mitigate hallucinations.

cs.PL↗